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• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX.Further information about EGA can be found on https://ega-archive.org and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access Policy Your Data Access Policy provides the terms and conditions of data use, this is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. Please consider the number of datasets that your submission consists of, for example, a case control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET)

  STARNET is a genetics of RNA expression study of multiple disease-relevant tissues obtained from living patients with cardiovascular disease. Tissue samples are obtained from blood, atherosclerotic-lesion-free internal mammary artery (MAM) and atherosclerotic aortic root (AOR), subcutaneous fat (SF), visceral abdominal fat (VAF), skeletal muscle (SKLM), and liver (LIV) during open thorax surgery of 600 coronary artery disease (CAD) patients. All patients gave written informed consent. The inclusion criterion was eligibility for coronary artery by-pass graft (CABG) surgery. Patients with other severe systemic diseases, such as active systemic inflammatory disease or cancer, were excluded. The primary clinical end points were the SYNTAX score based on the extent of coronary atherosclerosis assessed from preoperative angiograms. The STARNET patients are Caucasians (31% females); 32% had diabetes, 75% had hypertension, and 67% had hyperlipidemia; and 33% had an MI before age 60. By New York Heart Association criteria, 45% were class I, 42% class II, 9% class III, and 1% class IV. TYPES AND RNA SEQUENCING: 566 DNA genotype and 3577 RNA-seq profiles from seven tissues from 600 STARNET CABG patients passed quality control (on average 511 RNA-seq profiles/tissue). DNA was genotyped with the OmniExpress Exome array (Illumina, ~900k SNPs) and imputed to a total of 14,098,063 DNA variant calls (6,245,505 with minor allele frequency >5%). The STARNET subjects mainly overlap with Caucasian of Northern European (Finnish) descent. RNA sequencing was performed using the HighSeq2000 platform, poly-A (LIV, SKLM, VAF, SF and blood) and ribo-zero (AOR, MAM) protocols with 50-100 bp read lengths, single end to 15-30 million read depth.

  Study phs001203

• Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

  The Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss, which constitute the Epidemiology of the Weiss Cohort Projects, consist of a series of inter-connected projects, building upon a set of cohort projects of various groups, mainly drug users from medication-assisted treatment programs, that Dr. Stanley H. Weiss first developed in the 1980’s plus several newer initiatives, each with an array of collaborators. Beginning in the 1980’s, Dr. Stanley H. Weiss started several long-term studies of persons who inject drugs (PWID) across the United States, ultimately enrolling over 10,000 participants through the early 1990’s with an average age then in their 30’s. About a quarter were enrolled from sites in New Jersey (NJ). These studies included the first testing of PWID for the human immunodeficiency virus (HIV) and the human T-cell lymphotropic viruses (HTLV I and HTLV II). Cumulative past support (initiation thru ~ 1999) for these cohort studies included ~ $20 million from intramural resources from the National Cancer Institute (NCI) and the National Institute on Drug Abuse (NIDA), plus multiple grants and in-kind support from the New Jersey Department of Health (NJDOH) totaling ~ $1 million. The Weiss Cohort Projects include the first large AIDS-era cohorts to include women at high risk for HIV. A high percentage of subjects in these studies are black or Latino. Thus, this is an ethnically diverse US cohort, with a high proportion of women included. These subjects are at high risk of parenteral and sexual infection from both drug use and sexual practices. Samples from other studies conducted by Dr. Weiss, in which detailed interviews were conducted, are included as controls (persons documented by us not to have a history of opioid drug use). As one of our groups of subjects have many persons of Haitian ancestry, we specifically included some Haitians who had never used opioids as controls. Our documentation includes such ancestry. These cohorts demonstrated high rates of HIV and HTLV-II infection in PWIDs, including one study initiated in 1981 with confirmation in the later cohorts. In the first two decades of these studies, among numerous publications was the first study showing a very high rate of hepatitis C infection among PWIDs. An example of how the studies’ long-time horizon proved essential was that it first became possible to test whether a person had ever been infected with hepatitis C virus (HCV), as well as how much HCV was in each person’s blood, many years after the specimens were collected. This allowed HCV amounts in blood to be compared for subjects who had died of liver disease early in the study versus those who survived. Then a sequence of published papers culminated in demonstrating, using a nested case-control design, that a high baseline HCV titer was predictive of early progression to death from end-stage liver failure. Outcomes related to HCV (end stage liver disease and hepatocellular carcinoma) remain under study. In the original cohort studies, the mean age at enrollment was ~ 33 years old, so that those still alive in 2022 are mainly now ~ 60 - 75 years old. Many participants have already died. The tincture of time has led to subjects reaching ages when many more are dying from a wide array of outcomes, including from many chronic diseases (including cancer) as well as from infectious agents (especially HIV, HCV) or drug overdose. Renewed collaboration with local drug treatment programs has led to new field-based studies, including examination of some currently evolving problems among drug users. Dr. Weiss joined the National Institute on Drug Abuse (NIDA) Genetics Consortium (NGC) in 2017, and through the NIDA project officer has had access to NGC contract resources (see below). NIH Certificate of Confidentiality, CC-DA-16-214 (attached) protects these studies. Past arrangements related to data on our subjects leads to restrictions on the use of data emanating from our study, such as potential commercialization and restrictions on whom may access and use these data. NIDA Genetics Consortium (NGC) resources further support these endeavors and will be used as part of the NGC analyses studying the genetics of substance use. Study participants signed informed consent for the information collected from them to be used with no time limit and for biologic specimens collected from them to be used without restriction in future research. Serum samples were collected from participants, and from many also plasma, white blood cells and/or urine samples. About 100,000 vials were stored. All specimens have been continuously preserved at sufficiently cold temperatures to prevent deterioration, and many subjects separated white blood cells were processed and frozen in such a way as to maintain viability. Detailed data from the participants has been accumulated over time, and in general, linkage has been retained in each sub-study in accordance with the consent forms and protocols. For some participants, specimens were collected at multiple times (that is, sequential specimens). Multiple specimens from a single person exist in this database, and efforts at de-duplication remain ongoing. Dr. Weiss should be contacted if an investigator requires unique individuals since: • Multiple phases of enrollment occurred, and as our prospective follow-up continues; Dr. Weiss may identify new instances of multiple enrollment. • Some persons are related to each other. • In general, in this dataset for dbGaP, only a single specimen/record form a given person is included. Advances in laboratory testing techniques now permit innovative new uses for our linked research biospecimen repository. The ongoing focus of an interdisciplinary research program based on these cohorts relates subjects’ diseases, behaviors, medical history, and outcomes with biological and exposure markers. Participants’ use of various substances was ascertained on study enrollments, many serially over time. Quantitative frequency of use data, also sometimes sequential over time, were ascertained. Active ascertainment of outcomes is being conducted, including matching to mortality and cancer databases. Investigators interested in collaborations on specific outcomes (which is not part of this dbGaP dataset) or in the use of our stored specimens are encouraged to contact the principal investigator, Dr. Weiss. The processing of the genomic data was done in conjunction with NIDA, and in accordance with some longstanding data cleaning steps used by NIDA in the NIDA Genetics Consortium (NGC), a group to which we shall be contributing these data for collaborative analyses. Since there is the potential for these steps to introduce certain types of potential biases, we summarize these here. Under contract from NIDA, cryopreserved sera or plasma (-80 C) or cells (in liquid nitrogen) were used, with most stored having been stored for 30 to 40 years in our biorepository. In the case of serum or plasma, in which only (largely) cell-free DNA fragments were available, DNA was extracted and restored prior to amplification. Industry standard DNA amplification techniques were done on all samples prior to genotyping in accord with established protocols of the NIDA Genetics Consortium. Our genotype data were run and processed on the Illumina Infinium OmniExpress_v_1.3 array. This array has 714,238 SNPs, and was designed many years ago. There were 628 SNPs on the array that do not correspond to any chromosome position, and these were removed. Genotype data were submitted by NIDA’s contracted genotyping laboratory in six batches over time to NIDA’s contracted dbGaP data management group, which conducted quality control (QC) analyses. QC analysis included an assessment of batch effects on for five of the six batches. (One of the batches, with only 12 samples, was too small for QC analysis of batch effects.) Standard NIDA Genetic Consortium cleaning was performed. Samples with a call rate <.85 were removed. Only one sample per person was retained. When more than one specimen was genotyped from one subject, only the sample with the higher call rate was retained (provided, of course, that that call rate was ≥ 0.85). We have retained some people we know are related, including some found to have been related through genotyping; the pedigree file describes those relationships. In summary, key cleaning steps include: 1. Using PLINK to check gender discrepancy. 2. Using PREST-PLUS and KING (Kinship-based Inference for GWAS) to check relatedness. 3. Using PEDCHECK and PLINK to check/zero-out Mendelian error. 4. Using PLINK to perform sample QC, SNP QC, along with KING to perform chromosome X and chromosome Y QC. 5. SNP-QC: Batch-effect: 5 Batches were compared (one batch, with few samples, was not). These five batches were compared to each other in all ten possible pairs, one batch vs. another batch, examining SNP allele frequency discrepancies by population (from GRAF), Fisher Exact Allelic test, with the criterion of p<5e-8 for removal. 6. SNP-QC: discordant SNPs in QC duplicates. Compared 25 QC duplicated samples with call rate > 0.95, removed SNPs with 3+ discordance. 7. There were 1,056 SNPs that were monomorphic; these have been retained so they can be included in analyses in which our dbGaP data are combined with those from other cohorts (in the latter of which those SNPs may not be monomorphic). The final cleaned dataset submitted has 8,898 samples and 606,793 SNPs.

  Study phs002140

• TNBC ctDNA Targeted Panel

  Circulating tumour DNA (ctDNA) detection in liquid biopsy is an emerging alternative to tissue biopsy, but its utility in treatment response monitoring and prognosis in triple negative breast cancer (TNBC) remains to be well understood. In this study, we determined the presence of early stage TNBC ctDNA detectable actionable mutations with a clinically validated hotspot treatment indication panel. Sequencing of plasma DNA from 130 TNBC patients collected within 7 months of primary treatment completion revealed that 7.7% had detectable residual disease with a hotspot panel. Among neoadjuvant treated patients, we observed a trend where patients with incomplete pathologic response and positive ctDNA within 7 months of treatment completion were at risk of reduced progression free survival. We propose that a high risk subset of early TNBC patients treated in NAT protocols may be identifiable by combining tissue response and sensitive ctDNA detection.

  Study EGAS00001006937

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Mediators of Atherosclerosis in South Asians Living in America Study (MASALA)

  Cohort Description The Mediators of Atherosclerosis in South Asians Living in America (MASALA) study is a prospective cohort of South Asians that aims to identify risk factors for heart disease in a large, growing Asian American subgroup. MASALA enrolled 906 South Asians in 2010-2013 and then added a new wave of 258 South Asian participants from 2017-2018, for a full cohort size of 1,164. Data Being Submitted Wave 1 questionnaire data includes 3967 variables for up to 431 MASALA participants in C4R. Wave 2 questionnaire data includes 448 variables for up to 313 MASALA participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 248 MASALA participants in C4R. Derived data includes 43 variables for up to 528 MASALA participants in C4R. Phenotype data includes 113 variables for up to 429 MASALA participants in C4R.

  Study phs002980

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Atherosclerosis Risk in Communities Study (ARIC)

  Cohort Description The Atherosclerosis Risk in Communities (ARIC) study began in the mid-1980s with the initial aims being to describe the presence of subclinical atherosclerosis, the progression of atherosclerosis to clinical cardiovascular disease (CVD), and the association of novel risk factors with CVD. ARIC recruited its cohort of men and women aged 45-64 in 1987-89 from four communities (Forsyth County, NC; Jackson, MS; suburban Minneapolis, MN; and Washington County, MD). As of 2025, ARIC counts over 4,000 participants.Data Submitted Wave 1 questionnaire data includes 356 variables for up to 5763 ARIC participants in C4R. Wave 2 questionnaire data includes 367 variables for up to 5000 ARIC participants in C4R. Wave 3 questionnaire data includes 81 variables for up to 3450 participants in C4R.Dried Blood Spot/Serosurvey data includes 8 variables for up to 2437 ARIC participants in C4R. Derived data includes 62 variables for up to 18765 observations. Phenotype data includes 163 variables for up to 5937 ARIC participants in C4R. Brain MRI data includes 13 variables for up to 2550 participants in C4R.HCU data includes 21 variable for up to 631 ARIC participants in C4R.Lung CT data includes 15 variables for 1329 ARIC participants in C4R.Neurocognitive data includes 16 variables and 49826 observations.Serosurvey data includes 20 variables for 1707 ARIC participants in C4R.

  Study phs002988

• hereditary BrEAst Case CONtrol study (BEACCON)

  The BEACCON study aimed to address the lack of power of previous studies to identify novel BC predisposition genes by performing extensive sequencing in 12,000 women (11,511 analysed following exclusions) and further enhancing power by using an ‘extreme phenotype’ design with enrichment of familial non-BRCA1 and BRCA2 cases, compared with a control population of older women with ongoing confirmation of cancer-free status at June 2019. Three-quarters of the 1303 candidate genes screened were selected based on empiric evidence from local (69 multi-case BC families) or international whole exome sequencing studies, and the remainder were included to provide detailed coverage of functional pathways with established associations with BC.

  Dataset EGAD00001007025

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): REasons for Geographic and Racial Differences in Stroke (REGARDS)

  Cohort DescriptionThe REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort is one of the nation's largest, most comprehensive population-based cohorts, and it uses innovative home- and telephone-based data collection. REGARDS centrally recruited and initially examined 30,239 Black and White men and women aged ≥45 years in 2003-7 to understand why Southerners and Black Americans have a higher incidence of stroke and related diseases that affect brain health.Data Being Submitted Wave 1 questionnaire data includes 397 variables for 8109 REGARDS participants in C4R. Wave 2 questionnaire data includes 448 variables for 6421 REGARDS participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for 4058 REGARDS participants in C4R. Derived data includes 43 variables for 8606 REGARDS participants in C4R. Phenotype data includes 113 variables for 7880 REGARDS participants in C4R.

  Study phs002919

• Ribosome profiling shows variable sensitivity to detect open reading frames for conventional and different types of cryptic T cell antigens

  The aim of the study was to explore the sensitivity of ribosomal profiling to detect ORFs for cryptic antigens targeted by T cells in immune responses. Ribosomal profiling was performed for six EBV-LCLs. Detection of conventional and cryptic MiHA-encoding ORFs was compared. The data showed that open reading frames (ORFs) for conventional MiHAs as well as cryptic MiHAs in upstream ORFs were frequently detected with similar sensitivity. However, only a limited number of MiHAs in in reading frames alternative to protein-coding sequences and no ORFs derived from long non-coding RNAs were detected. In conclusion, Ribo-seq for identification of antigen-encoding ORFs is useful to screen for cryptic antigens in upstream ORFs as potential targets for immunotherapy.

  Study EGAS50000000322

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Severe Asthma Research Program (SARP)

  Cohort Description The Severe Asthma Research Program (SARP) has been investigating the clinical, physiologic and molecular phenotypes of asthma since 2000. It is currently following ~400 deeply phenotyped asthma patients. Data Being Submitted Wave 1 questionnaire data includes 397 variables for up to 375 SARP participants in C4R.Wave 2 questionnaire data includes 448 variables for up to 289 SARP participants in C4R.Dried Blood Spot/Serosurvey data includes 7 variables for up to 290 SARP participants in C4R.Derived data includes 43 variables for up to 463 SARP participants in C4R.Phenotype data includes 113 variables for up to 463 SARP participants in C4R.

  Study phs002913